Sentence examples for many hypothesis tests from inspiring English sources
Procedures for controlling the false positive rate when performing many hypothesis tests are commonplace in health and medical studies.
As illustrated in this study, many hypothesis tests that use genomics data prove strikingly significant.
All of the differences were statistically significant even when applying the Bonferroni correction in order to avoid making Type I errors because of the many hypothesis tests in the table.
However, studying each component of the score separately would require many more hypothesis tests, which we concluded that our study is not powered to accommodate.
The multiple testing problem arises because, if many hypotheses are tested simultaneously, some test statistics will be surprisingly extreme, even if no associations exist.
This is intermediate between using a raw p value (least conservative) and Bonferroni correction (most conservative), and accounts for the fact that many of the genotypes are in partial LD and, thus, many of the hypothesis tests are correlated.
In DIRECT, we are likely to undertake many thousands of hypothesis tests; therefore, determining true from false positive findings will rely on replication studies (in existing epidemiological cohorts linked to DIRECT) and validation studies (in clinical trials that will be undertaken in the second stage of the DIRECT project).
Because many hypotheses are tested simultaneously, the probability of rejecting the null hypothesis for any motif by chance at least once increases with the number of hypotheses tested.
4) There are many hypotheses being tested in this replication.
If many hypotheses are tested, inevitably some null hypotheses will be rejected just by chance (e.g., 5%% of null hypotheses if the chosen level of significance for individual hypotheses is p < 0.05).
When many hypotheses are tested, such as expression data of thousands of genes being compared between different groups, FWER increases dramatically, and is typically much larger than the significance level at which the individual hypotheses are tested.
